SoroushVahidi/scidocs huggingface dataset:
Assessment of lexical semantic judgment abilities in alcohol-dependent subjects: An fMRI study
(2013)

Neuropsychological studies have shown that alcohol dependence is associated with neurocognitive deficits in tasks requiring memory, perceptual motor skills, abstraction and problem solving, whereas language skills are relatively spared in alcoholics despite structural abnormalities in the language-related brain regions. To investigate the preserved mechanisms of language processing in alcohol-dependents, functional brain imaging was undertaken in healthy controls (n=18) and alcohol-dependents (n=16) while completing a lexical semantic judgment task in a 3 T MR scanner. Behavioural data indicated that alcohol-dependents took more time than controls for performing the task but there was no significant difference in their response accuracy. fMRI data analysis revealed that while performing the task, the alcoholics showed enhanced activations in left supramarginal gyrus, precuneus bilaterally, left angular gyrus, and left middle temporal gyrus as compared to control subjects. The extensive activations observed in alcoholics as compared to controls suggest that alcoholics recruit additional brain areas to meet the behavioural demands for equivalent task performance. The results are consistent with previous fMRI studies suggesting compensatory mechanisms for the execution of task for showing an equivalent performance or decreased neural efficiency of relevant brain networks. However, on direct comparison of the two groups, the results did not survive correction for multiple comparisons; therefore, the present findings need further exploration.
Markov Random Field model for single image defogging
(2013)

Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white background, called the atmospheric veil. As pointed in [1], Advanced Driver Assistance Systems (ADAS) based on the display of defogged images from a camera may help the driver by improving objects visibility in the image and thus may lead to a decrease of fatality and injury rates. In the last few years, the problem of single image defogging has attracted attention in the image processing community. Being an ill-posed problem, several methods have been proposed. However, a few among of these methods are dedicated to the processing of road images. One of the first exception is the method in [2], [1] where a planar constraint is introduced to improve the restoration of the road area, assuming an approximately flat road. The single image defogging problem being ill-posed, the choice of the Bayesian approach seems adequate to set this problem as an inference problem. A first Markov Random Field (MRF) approach of the problem has been proposed recently in [3]. However, this method is not dedicated to road images. In this paper, we propose a novel MRF model of the single image defogging problem which applies to all kinds of images but can also easily be refined to obtain better results on road images using the planar constraint. A comparative study and quantitative evaluation with several state-of-the-art algorithms is presented. This evaluation demonstrates that the proposed MRF model allows to derive a new algorithm which produces better quality results, in particular in case of a noisy input image.
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification
(2015)

The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with LogEuclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geometrical structure of the space of SPD matrix logarithms but also brings low efficiency especially when the dimensionality of SPD matrix is high. To overcome this limitation, we propose a novel metric learning approach to work directly on logarithms of SPD matrices. Specifically, our method aims to learn a tangent map that can directly transform the matrix logarithms from the original tangent space to a new tangent space of more discriminability. Under the tangent map framework, the novel metric learning can then be formulated as an optimization problem of seeking a Mahalanobis-like matrix, which can take the advantage of traditional metric learning techniques. Extensive evaluations on several image set classification tasks demonstrate the effectiveness of our proposed metric learning method.
Classifying streaming of Twitter data based on sentiment analysis using hybridization
(2018)

Twitter is a social media that developed rapidly in today’s modern world. As millions of Twitter messages are sent day by day, the value and importance of developing a new technique for detecting spammers become significant. Moreover, legitimate users are affected by means of spams in the form of unwanted URLs, irrelevant messages, etc. Another hot topic of research is sentiment analysis that is based on each tweet sent by the user and opinion mining of the customer reviews. Most commonly natural language processing is used for sentiment analysis. The text is collected from user’s tweets by opinion mining and automatic sentiment analysis that are oriented with ternary classifications, such as “positive,” “neutral,” and “negative.” Due to limited size, unstructured nature, misspells, slangs, and abbreviations, it is more challenging for researchers to find sentiments for Twitter data. In this paper, we collected 600 million public tweets using URL-based security tool and feature generation is applied for sentiment analysis. The ternary classification is processed based on preprocessing technique, and the results of tweets sent by the users are obtained. We use a hybridization technique using two optimization algorithms and one machine learning classifier, namely particle swarm optimization and genetic algorithm and decision tree for classification accuracy by sentiment analysis. The results are compared with previous works, and our proposed method shows a better analysis than that of other classifiers.
Semi-autonomous flying robot for physical interaction with environment
(2010)

This contribution presents the first results of the development of an unmanned aerial vehicle (UAV) which is capable of applying force to a wall while maintaining flight stability. This is a novel idea since UAVs are used so far only for tasks without physical contact to the surrounding objects. The basis for the work presented is a quadrotor system which is stabilized with an inertial measurement unit. As a new approach an additional actuator was added to generate forces in physical contact while the UAV stays horizontal. A control architecture based on ultrasonic distance sensors and a CMOS-camera is proposed. The performance of the system was proved by several flight tests. Potential applications of the system can be physical tasks at high places like cleaning windows or walls as well as rescue or maintenance tasks.
Bi-Modal DRAM Cache: Improving Hit Rate, Hit Latency and Bandwidth
(2014)

In this paper, we present Bi-Modal Cache - a flexible stacked DRAM cache organization which simultaneously achieves several objectives: (i) improved cache hit ratio, (ii) moving the tag storage overhead to DRAM, (iii) lower cache hit latency than tags-in-SRAM, and (iv) reduction in off-chip bandwidth wastage. The Bi-Modal Cache addresses the miss rate versus off-chip bandwidth dilemma by organizing the data in a bi-modal fashion - blocks with high spatial locality are organized as large blocks and those with little spatial locality as small blocks. By adaptively selecting the right granularity of storage for individual blocks at run-time, the proposed DRAM cache organization is able to make judicious use of the available DRAM cache capacity as well as reduce the off-chip memory bandwidth consumption. The Bi-Modal Cache improves cache hit latency despite moving the metadata to DRAM by means of a small SRAM based Way Locator. Further by leveraging the tremendous internal bandwidth and capacity that stacked DRAM organizations provide, the Bi-Modal Cache enables efficient concurrent accesses to tags and data to reduce hit time. Through detailed simulations, we demonstrate that the Bi-Modal Cache achieves overall performance improvement (in terms of Average Normalized Turnaround Time (ANTT)) of 10.8%, 13.8% and 14.0% in 4-core, 8-core and 16-core workloads respectively.
Intangible benefits valuation in ERP projects
(2002)

The development, implementation and ownership of information systems, especially large-scale systems such as enterprise resource planning (ERP), has become progressively longer in duration and more cost intensive. As a result, IS managers are being required to justify projects financially based on their return. Historically, information systems have been difficult to quantify in monetary terms because of the intangible nature of many of the derived benefits, e.g. improved customer service. Using the case study methodology, this paper examines an attempt by a large computer manufacturer to incorporate intangibles into traditional cost–benefit analysis in an ERP project. The paper reviews the importance of intangibles, lists intangible benefits that are important in ERP projects and demonstrates the use of a scheme through which they can be incorporated into traditional evaluation techniques.
Modeling of electric vehicle loads for power flow analysis based on PSAT
(2016)

This paper proposes the modeling of electric vehicle(EV) loads for power flow analysis based on User Define Models (UDMs) in power system analysis toolbox (PSAT) programming, According to the development of EV technology especially motor controller and battery, the EV has totally designed and made for using in city area as small city cars. To study the performance of EV, this aims to present the model of EV loads for using to solve power flow analysis in the real power system. EV load is modeled as the function box equivalent on PSAT that can connect to the single line diagram of power system model. The IEEE 14 bus system is selected for simulating the performance of EV and charging station which compared between PQ load base case and EV load. The simulation result showed that the EV load was installed at bus No.14. The rated power of EV load was increased from 5.0 p.u. to 79 p.u. This proposed EV load model can be used to solve the power flow analysis with continuation power flow (CPF) method. The EV load model is directly effected to voltage stability margin when the EV load increased. This study can be verified that proposed EV load can use to study the EV load in the future works.
Modular equalization architecture using inter-module and switchless intra-module equalizer for energy storage system
(2015)

This paper proposes a novel modular equalization architecture using inter-module and intra-module equalizers for energy storage systems comprising multiple cells/modules connected in series. In the proposed equalization architecture, cells in a module is equalized by a series-resonant voltage multiplier (SRVM)-based intra-module equalizer while module voltages are unified by a switched capacitor converter (SCC)-based inter-module equalizer. A square wave voltage generated at a switching node of the SCC-based inter-module equalizer is utilized to drive the SRVM-based intra-module equalizer so that the SRVM-based intra-module equalizer can be totally switchless topology. The required switch count of the proposed equalization architecture can be dramatically reduced compared to conventional architectures, allowing simplified circuitry. The proposed equalization architecture is modular and allows different equalizer topologies to be used for inter- and intra-module equalizers, offering good modularity and design flexibility. The derivation procedure of the proposed equalization architecture is explained, followed by separate analyses for the SCC-based inter-module equalizer and SRVM-based intra-module equalizer. An experimental equalization test for three supercapacitor (SC) modules, each consisting of four cells in series, was performed from an initially voltage-imbalanced condition. Module and cell voltages were equalized at different rates, and the equalization performance of the proposed modular equalization architecture was successfully demonstrated.
Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis
(2009)

This letter presents theoretical, algorithmic, and experimental results about nonnegative matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is underlaid by a well-defined statistical model of superimposed gaussian components and is equivalent to maximum likelihood estimation of variance parameters. This setting can accommodate regularization constraints on the factors through Bayesian priors. In particular, inverse-gamma and gamma Markov chain priors are considered in this work. Estimation can be carried out using a space-alternating generalized expectation-maximization (SAGE) algorithm; this leads to a novel type of NMF algorithm, whose convergence to a stationary point of the IS cost function is guaranteed. We also discuss the links between the IS divergence and other cost functions used in NMF, in particular, the Euclidean distance and the generalized Kullback-Leibler (KL) divergence. As such, we describe how IS-NMF can also be performed using a gradient multiplicative algorithm (a standard algorithm structure in NMF) whose convergence is observed in practice, though not proven. Finally, we report a furnished experimental comparative study of Euclidean-NMF, KL-NMF, and IS-NMF algorithms applied to the power spectrogram of a short piano sequence recorded in real conditions, with various initializations and model orders. Then we show how IS-NMF can successfully be employed for denoising and upmix (mono to stereo conversion) of an original piece of early jazz music. These experiments indicate that IS-NMF correctly captures the semantics of audio and is better suited to the representation of music signals than NMF with the usual Euclidean and KL costs.
The Life Satisfaction Advantage of Being Married and Gender Specialization
(2015)

This investigation examined whether the life satisfaction advantage of married over unmarried persons decreased over the last three decades, and whether the changes in the contextual gender specialization explained this trend. The author used representative data from the World Values Survey–European Values Study (WVS–EVS)-integrated data set for 87 countries (N = 292,525) covering a period of 29 years. Results showed that the life satisfaction advantage of being married decreased among men but not among women. The analysis did not support the hypothesis that contextual gender specialization shaped the observed trend. Only in developed countries the declining contextual specialization correlated with smaller life satisfaction advantage of being married. This evidence suggests that the advantages of marriage are greater under conditions that support freedom of choice rather than economic necessity. (130 words)
UFMC system performance analysis for discrete narrow-band private networks
(2015)

In this paper, the performance of universal-filtered multi-carrier (UFMC) system is analyzed. Specifically, the UFMC signals are generated with Dolph-Chebyshev, Hamming and Hanning filtering. The characteristics of UFMC signals are also compared with OFDM signal, in both software and hardware-in-the-loop simulations. Experimental results show that UFMC signals have better performance than OFDM signal, by means of side-lobe attenuation, bit-error-ratio (BER) and Error Vector Magnitude (EVM). The results indicate that the UFMC signal is more suitable than OFDM signal for discrete narrow-band private networks. The results can provide useful information on the design of multi-carrier radio systems.
Following My Head and My Heart: Integrating Preschoolers' Empathy, Theory of Mind, and Moral Judgments.
(2017)

Associations among hypothetical, prototypic moral, and conventional judgments; theory of mind (ToM); empathy; and personal distress were examined in 108 socioeconomically diverse preschoolers (Mage  = 42.94 months, SD = 1.42). Repeated measures analysis of covariance with empathy, false beliefs, and their interaction as covariates indicated that empathy was significantly associated with judgments of greater moral but not conventional transgression severity, particularly for psychological harm, and with deserved punishment for unfairness. False beliefs were associated with (combined) moral criterion judgments of rule and authority independence and inalterability. Empathy also was positively associated with criterion judgments but only for children low in ToM. Personal distress was unrelated to judgments. Results demonstrate the importance of both affective and cognitive processes in preschoolers' moral judgments.
A 5-MHz 91% Peak-Power-Efficiency Buck Regulator With Auto-Selectable Peak- and Valley-Current Control
(2010)

This paper presents a multi-MHz buck regulator for portable applications using an auto-selectable peak- and valley-current control (ASPVCC) scheme. The proposed ASPVCC scheme can enable the current-mode buck regulator to reduce the settling-time requirement of the current sensing by two times. In addition, the dynamically biased shunt feedback technique is employed to improve the sensing speed and the sensing accuracy of both the peak and valley current sensors. With both ASPVCC scheme and advanced current sensors, the buck regulator can thus operate at high switching frequencies with a wide range of duty ratios for reducing the required off-chip inductance. The proposed current-mode buck regulator was fabricated in a standard 0.35-μm CMOS process and occupies a small chip area of 0.54 mm2. Measurement results show that the buck regulator can deliver a maximum output current of 500 mA, operate at 5 MHz with a wide duty-ratio range of about 0.6, use a small-value off-chip inductor of 1 μH, and achieve the peak power efficiency of 91%.
Low-Quality Product Review Detection in Opinion Summarization
(2007)

Product reviews posted at online shopping sites vary greatly in quality. This paper addresses the problem of detecting lowquality product reviews. Three types of biases in the existing evaluation standard of product reviews are discovered. To assess the quality of product reviews, a set of specifications for judging the quality of reviews is first defined. A classificationbased approach is proposed to detect the low-quality reviews. We apply the proposed approach to enhance opinion summarization in a two-stage framework. Experimental results show that the proposed approach effectively (1) discriminates lowquality reviews from high-quality ones and (2) enhances the task of opinion summarization by detecting and filtering lowquality reviews.
Automatically Producing Plot Unit Representations for Narrative Text
(2010)

In the 1980s, plot units were proposed as a conceptual knowledge structure for representing and summarizing narrative stories. Our research explores whether current NLP technology can be used to automatically produce plot unit representations for narrative text. We create a system called AESOP that exploits a variety of existing resources to identify affect states and applies “projection rules” to map the affect states onto the characters in a story. We also use corpus-based techniques to generate a new type of affect knowledge base: verbs that impart positive or negative states onto their patients (e.g., being eaten is an undesirable state, but being fed is a desirable state). We harvest these “patient polarity verbs” from a Web corpus using two techniques: co-occurrence with Evil/Kind Agent patterns, and bootstrapping over conjunctions of verbs. We evaluate the plot unit representations produced by our system on a small collection of Aesop’s fables.
Plot Units and Narrative Summarization
(1981)

In order to summarize a story, i t is necessary to access a high level analysis of the story that highlights its central concepts. A technique of memory representation based on plot units appears to provide a rich foundation for such an analysis. Plot units are conceptual structures that overlap with each other when a narrative is cohesive. When overlapping intersections between plot units are interpreted as arcs in a graph of plot units, the resulting graph encodes the plot of the story. Structural features of the graph then reveal which concepts are central to the story, and which concepts are peripheral. Plot unit analysis is currently being investigated as a processing strategy for narrative summarization by both computer simulotion ond psychological experiments.
Building a Bank of Semantically Encoded Narratives
(2010)

We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis of a “story bank” resource that would facilitate the automatic analysis of narrative structure and content. The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet. Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content. Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions. We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story. Finally, we describe ongoing work toward extending the method to integrate the annotator’s interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text).
Constructing inferences during narrative text comprehension.
(1994)

The authors describe a constructionist theory that accounts for the knowledge-based inferences that are constructed when readers comprehend narrative text. Readers potentially generate a rich variety of inferences when they construct a referential situation model of what the text is about. The proposed constructionist theory specifies that some, but not all, of this information is constructed under most conditions of comprehension. The distinctive assumptions of the constructionist theory embrace a principle of search (or effort) after meaning. According to this principle, readers attempt to construct a meaning representation that addresses the reader's goals, that is coherent at both local and global levels, and that explains why actions, events, and states are mentioned in the text. This study reviews empirical evidence that addresses this theory and contrasts it with alternative theoretical frameworks.